How Attribution Benchmarks Can Help You Measure ROI and Maximize ROAS

Are you benchmarking your campaigns as a marketer? If not, you’re missing out on a golden opportunity to see how your campaigns are performing compared to the industry standard. Learning the truth about how your campaigns are performing is essential to your success as a marketer. If they’re in the average range or even outpacing it, then you can report back success to your manager. If they’re not performing as well as the rest of the market, then you can take action to improve them and increase your return on investment. Either way, taking benchmarks into consideration can only help you prove and improve your ROAS as a marketer.

Footfall Attribution Benchmarks

Now that you know the importance of considering benchmarks when you evaluate your campaigns, let’s focus on footfall attribution benchmarks, specifically. We recently released our 2019 Footfall Attribution Benchmarks report, which you can find below, to help you see how your campaigns stack up against the rest of the market.

If you’ve been following our blog, then you’ll know this is not the first year we’ve released footfall attribution benchmarks. As with the benchmarks we released in 2018 and before, this study provides updated benchmarks for several key metrics:

1. Brand Uplift

Through brand uplift, we show the impact of ad exposure in driving in-store visits. You can gain post-campaign insights on whether your campaign actually drove “uplift,” which you can use to prove ROAS, as you can often link a given store visit to an actual sale.

2. Visit Rate

Visit rate represents the percentage of consumers exposed to the campaign that visited the store out of all exposed consumers. This is an important metric because it reveals how many consumers your brand is resonating with enough to drive them to your store.

3. CPIV (Cost Per Incremental Visit)

With CPIV, or cost per incremental visit, we show the necessary budget spent to receive one incremental visit from the exposed group compared to visits from the control group. When you’re planning how you’d like to allocate your budget for the coming year, CPIV is a helpful metric to consider as it shows exactly how much each additional store visit will cost you.

4. Dwell Time

Dwell time is a term that Cuebiq created to measure time spent in store, which is used to verify a consumer visit by distinguishing actual visits from non-relevant data points or “fake visits” as we call them. In order to determine whether users actually visited a POI or were just passing by, you need to consider how long they spent at that location.

5. Time of Visit

In this report, time of visit means the time that consumers visited the POI, based on local time.

6. Case Studies

We provide a variety of case studies across different verticals — from Convenience Stores to Casual Dining to Home Furniture — that showcase the above metrics.

What’s New in This Year’s Report

This year, we’ve also added several new elements to our benchmarks report, in response to demand for more vertical industries and use cases across different channels. Here’s what’s new this year:

Along with the updated benchmarks, Cuebiq is now able to provide year-to-year comparisons for the metrics in the report. This enables you to see how certain industries as a whole are performing over time in terms of driving store visits with their advertising campaigns.

New retail categories such as Alcohol/ Beverages, Apparel/Fashion, Beauty, Fine Dining, Home Furniture, Jewelry Stores, and Tourism are now included.

OOH benchmarks use cases are now included.

New case studies ranging from digital to OOH and TV are now included.

Offline Intelligence: The Cuebiq Attribution Difference

Based on this report, it’s undeniable that offline intelligence has become a powerful solution to tie together signals across channels, because it helps marketers measure in-store footfall. The more marketers leverage offline intelligence, the easier they can answer broader questions to inform their marketing strategies and better understand real-world, in-store experiences.

Cuebiq’s data collection methodology and intelligence platform allow brands to go much deeper when evaluating their campaigns. In order to map, target and measure the offline consumer journey, Cuebiq applies proprietary machine-learning algorithms that distill the vast amount of location data collected into “visit data.” This is possible thanks to the platform’s ability to understand if multiple signals originated from the same location over time and, based on certain parameters, determine if they should be classified as one visit.

For example, Cuebiq may collect six data points over three hours for the same anonymous user, all from one movie theater, and can determine that this was one visit. Conversely, if Cuebiq only detects one data point over 10 minutes at a movie theater, the intelligence platform will determine that this was not a visit.

These kinds of insights are leveraged in each Cuebiq Attribution report, giving brands benchmarks to get a sense of engagement for consumers who visit their locations after being exposed to an ad. This helps marketers not only prove success but maximize ROAS for their future campaigns.

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About the Author

Isabel Sperry, Manager, Brand Marketing

Isabel is a digital marketer with a background in blogging, graphic design, and social media management. A graduate of Yale University, she majored in American Studies and is passionate about American literature and art history.